# Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit

arXiv: https://arxiv.org/abs/2201.10079 · HTML: https://mlsanity.com/paper/2201.10079/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches](https://mlsanity.com/paper/1906.11463/) (2019)
- [Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps](https://mlsanity.com/paper/1807.10584/) (2018)
- [An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN](https://mlsanity.com/paper/1809.01263/) (2018)
- [AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy](https://mlsanity.com/paper/1909.02477/) (2019)
- [Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy](https://mlsanity.com/paper/2010.06034/) (2020)

## Built on by (most cited first, 4 of 4)

- [Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images](https://mlsanity.com/paper/2311.05021/) (2023)
- [Generalizing monocular colonoscopy image depth estimation by uncertainty-based global and local fusion network](https://mlsanity.com/paper/2409.15006/) (2024)
- [Polyp segmentation in colonoscopy images using DeepLabV3++](https://mlsanity.com/paper/2407.19327/) (2024)
- [EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment](https://mlsanity.com/paper/2608.04472/) (2026)

## Abstract (arXiv)

Automatic detection of polyps is challenging because different polyps vary greatly, while the changes between polyps and their analogues are small. The state-of-the-art methods are based on convolutional neural networks (CNNs). However, they may fail due to lack of training data, resulting in high rates of missed detection and false positives (FPs). In order to solve these problems, our method combines the two-dimensional (2-D) CNN-based real-time object detector network with spatiotemporal information. Firstly, we use a 2-D detector network to detect static images and frames, and based on the detector network, we propose two feature enhancement modules-the FP Relearning Module (FPRM) to make the detector network learning more about the features of FPs for higher precision, and the Image Style Transfer Module (ISTM) to enhance the features of polyps for sensitivity improvement. In video detection, we integrate spatiotemporal information, which uses Structural Similarity (SSIM) to measure the similarity between video frames. Finally, we propose the Inter-frame Similarity Correlation Unit (ISCU) to combine the results obtained by the detector network and frame similarity to make the final decision. We verify our method on both private databases and publicly available databases. Experimental results show that these modules and units provide a performance improvement compared with the baseline method. Comparison with the state-of-the-art methods shows that the proposed method outperforms the existing ones which can meet real-time constraints. It's demonstrated that our method provides a performance improvement in sensitivity, precision and specificity, and has great potential to be applied in clinical colonoscopy.